我正在Windows 10上的Anaconda 64位上运行Matlab的python 2.7代码（包含GPy和GPyOpt，高斯过程和贝叶斯优化的Python实现），我遇到了以下错误：
静止警告：无法导入cython模块：使用stationary>_gradients_X_cython时出错（第323行）Python错误：Name错误：全局名称'stationary_cython'未定义
当我在python中运行代码时，我没有任何问题，但是当我从MATLAB中调用脚本时，问题就来了（我几个月前从MATLAB中运行代码没有任何问题。
我不得不提，最近由于某些原因，我将numpy降级为Numpy=1.11.0。这是因为Matlab在最新版本的Numpy中有一个bug。
我还面临着以下窗口：应用程序试图加载C运行时库...

**Q：**请问您能帮我解决这个问题吗？

Python Error: NameError: global name 'stationary_cython' is not defined

    Error in stationary>gradients_X (line 236)
            return self._gradients_X_cython(dL_dK, X, X2)

    Error in kernel_slice_operations>wrap (line 118)
            ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2))

    Error in prod>gradients_X (line 80)
            target += self.parts[0].gradients_X(dL_dK*self.parts[1].K(X, 
    X2), X, X2)

   Error in kernel_slice_operations>wrap (line 118)
            ret = s.handle_return_array(f(self, dL_dK, s.X, s.X2))

   Error in gp>predictive_gradients (line 337)
            mean_jac[:,:,i] = 
   kern.gradients_X(self.posterior.woodbury_vector[:,i:i+1].T, Xnew, 
   self._predictive_variable)

   Error in gpmodel>predict_withGradients (line 113)
        dmdx, dvdx = self.model.predictive_gradients(X)

   Error in EI>_compute_acq_withGradients (line 47)
        m, s, dmdx, dsdx = self.model.predict_withGradients(x)

   Error in base>acquisition_function_withGradients (line 46)
        f_acqu,df_acqu = self._compute_acq_withGradients(x)
   Error in LP>d_acquisition_function (line 128)
            _, grad_acq_x = self.acq.acquisition_function_withGradients(x)

Error in LP>acquisition_function_withGradients (line 139)
        aqu_x_grad = self.d_acquisition_function(x)

Error in acquisition_optimizer>fp_dfp (line 165)
            fp_xx , dfp_xx = f_df(xx)

Error in optimizer>_f_df (line 60)
                return f(x), f_df(x)[1][0]

Error in optimize>__call__ (line 63)
        fg = self.fun(x, *args)

Error in optimize>function_wrapper (line 289)
        return function(*(wrapper_args + args))

Error in lbfgsb>func_and_grad (line 278)
            f = fun(x, *args)

Error in lbfgsb>_minimize_lbfgsb (line 330)
                f, g = func_and_grad(x)

Error in lbfgsb>fmin_l_bfgs_b (line 193)
                           **opts)

Error in optimizer>optimize (line 64)
            res = scipy.optimize.fmin_l_bfgs_b(_f_df, x0=x0, bounds=self.space.get_bounds(), maxiter=self.maxiter)

Error in acquisition_optimizer>optimize (line 177)
                x_min, f_min = self.optimizer.optimize(x0, f =fp, df=None, f_df=fp_dfp)

Error in base>optimize (line 59)
            out = self.optimizer.optimize(f=self.acquisition_function, f_df=self.acquisition_function_withGradients)[0]

Error in batch_local_penalization>compute_batch (line 34)
        X_batch = self.acquisition.optimize()

Error in bo>_compute_next_evaluations (line 186)
        return self.evaluator.compute_batch()

Error in bo>run_optimization (line 108)
            self.suggested_sample = self._compute_next_evaluations()

Error in bayesian_optimization>run_optimization (line 458)
        super(BayesianOptimization, self).run_optimization(max_iter = max_iter, max_time = max_time,  eps = eps,
        verbosity=verbosity, save_models_parameters = save_models_parameters, report_file = report_file, evaluations_file=
        evaluations_file, models_file=models_file)

Error in bayesian_optimization>__init__ (line 244)
        self.run_optimization(max_iter=0,verbosity=self.verbosity)

Error in BatchBO>BAYESOPT2 (line 37)
                                                 acquisition_weight = 2)